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Record W2296539364

Exploring the effectiveness of a novel teaching approach for information and academic literacies in a first year engineering unit

2015· article· en· W2296539364 on OpenAlexaboutno aff
Mariette LeRoux, Fiona Jones, Nicholas M. K. Tse, Daniel McGill, Azadeh Safari, Sudipta Chakraborty

Bibliographic record

VenueProceedings of The Australian Conference on Science and Mathematics Education (formerly UniServe Science Conference) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacyTransferable skills analysisSession (web analytics)Relevance (law)Process (computing)Computer scienceUnit (ring theory)Engineering educationMathematics educationLiteracyPsychologyPedagogyHigher educationEngineeringWorld Wide WebEngineering management
DOInot available

Abstract

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First year Engineering students tend to lack key information and academic literacy skills, which results in poor writing and language use, the use of a limited range of sources and poor referencing in their assignments. In the 2014 graduate outlook survey, 48% of graduate employers ranked communication skills as the most important selection criterion when recruiting graduates. Transferable skills are becoming increasingly important, not just to produce a more adaptable work force, but to inspire lifelong students who will continuously learn and improve. In the Macquarie University Engineering Program these transferable skills are introduced early in the degree using enquiry based methodology in a core first year Engineering unit. Tutors play a pivotal role in this process, facilitating repeated practice and acting as mentors for the students. To emphasise the importance of information and academic literacy as the first step in educating Engineering students, librarians developed a series of ‘research studios’ based on Baratta, Chong and Foster’s work (2011) which were run during tutorial sessions in week 4 of session 1. As Engineering students typically have active, sensing, inductive and visual learning styles (Young, 2012, p. 22) an activity based approach was used to help students self-discover and practice. This was supplemented with an online language activity created by the learning skills department. The following learning outcomes were addressed:  recognising when information is needed  appreciating the relevance of different types of resources for their field  identifying the most efficient search strategy to locate relevant information of a high standard  critically evaluating information sources  using appropriate academic language  using the correct format of reporting  referencing correctly and ethically Over 300 students attended tutorials held in library classrooms. Each ‘research studio’ was held in a different room and facilitated by a different library staff member, with groups of students moving from room to room at the conclusion of each 40 minute session. Library staff members provided short instruction, with most of the tutorial time devoted to hands-on activities, small group work and discussion. A large first year core unit was chosen to pilot this approach in order to be representative of the Engineering student population. Evaluation data shows that all of the activities had positive effects on student learning. The online language activity recorded a high number of hits. Student feedback indicated that the activity-based approach to developing information skills helped to consolidate understanding. Tutor feedback indicated that the quality of assignments submitted following the program was improved over previous sessions. To facilitate integrating these literacies into the unit, tutors will provide input into reviewing the exercises and will be trained to facilitate the learning activities co-designed by librarians and learning skills specialists in a blended learning format. As there was some feedback that exercises were too easy, the input from tutors will help pitch the training at the appropriate level and also provide valuable subject specific context. This presentation shares the results of this unique collaboration and its impact on student test results. It will address the information and academic literacy skills that Engineering students require to succeed in their academic and professional endeavours. REFERENCES Ali, R., Abu Hassan, N., Daud, M. Y. M., & Jusoff, K. (2010). Information literacy skills of engineering students. International Journal of Research and Reviews in Applied Sciences, 5(3), 264-270. http://eprints.utm.my/37881/2/IJRRAS_5_3_08.pdf Baratta, M., Chong, A & Foster, J.A. (2011). The research studio: integrating information literacy into a first year engineering science course. American Society for Engineering Education Conference, Vancouver, Canada. file:///C:/Users/mariette.leroux/Downloads/ASEE2011TheResearchStudioFinal%20(2).pdf Fosmire, M & Radcliffe, D. (2013). Integrating information into the Engineering design process. West Lafayette, IN.: Perdue University Press. http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1030&context=purduepress_ebooks Lindsay, E. (2015). Graduate outlook 2014: employer’s perspectives on graduate recruitment in Australia. Melbourne, Vic.: Graduate Careers Australia. http://www.graduatecareers.com.au/wp-content/uploads/2015/06/Graduate_Outlook_2014.pdf Young, S.J. (2012). Engineering. in O’Clair and Davidson, J. (eds) The busy librarian’s guide to information literacy in science and engineering. Chicago, IL: Association of College and Research Libraries Proceedings of the Australian Conference on Science and Mathematics Education, Curtin University, Sept 30th to Oct 1st, 2015, page X, ISBN Number 978-0-9871834-4-6.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.177
GPT teacher head0.347
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2015
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